17 research outputs found
Learning Collective Behavior in Multi-relational Networks
With the rapid expansion of the Internet and WWW, the problem of analyzing social media data has received an increasing amount of attention in the past decade. The boom in social media platforms offers many possibilities to study human collective behavior and interactions on an unprecedented scale. In the past, much work has been done on the problem of learning from networked data with homogeneous topologies, where instances are explicitly or implicitly inter-connected by a single type of relationship. In contrast to traditional content-only classification methods, relational learning succeeds in improving classification performance by leveraging the correlation of the labels between linked instances. However, networked data extracted from social media, web pages, and bibliographic databases can contain entities of multiple classes and linked by various causal reasons, hence treating all links in a homogeneous way can limit the performance of relational classifiers. Learning the collective behavior and interactions in heterogeneous networks becomes much more complex. The contribution of this dissertation include 1) two classification frameworks for identifying human collective behavior in multi-relational social networks; 2) unsupervised and supervised learning models for relationship prediction in multi-relational collaborative networks. Our methods improve the performance of homogeneous predictive models by differentiating heterogeneous relations and capturing the prominent interaction patterns underlying the network structure. The work has been evaluated in various real-world social networks. We believe that this study will be useful for analyzing human collective behavior and interactions specifically in the scenario when the heterogeneous relationships in the network arise from various causal reasons
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Advances in knowledge discovery and data mining Part II
19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p
Improving accuracy of recommender systems through triadic closure
The exponential growth of social media services led to the information overload problem
which information filtering and recommender systems deal by exploiting various techniques.
One popular technique for making recommendations is based on trust statements between
users in a social network. Yet explicit trust statements are usually very sparse leading to the
need for expanding the trust networks by inferring new trust relationships. Existing methods
exploit the propagation property of trust to expand the existing trust networks; however, their
performance is strongly affected by the density of the trust network. Nevertheless, the
utilisation of existing trust networks can model the users’ relationships, enabling the inference
of new connections. The current study advances the existing methods and techniques on
developing a trust-based recommender system proposing a novel method to infer trust
relationships and to achieve a fully-expanded trust network. In other words, the current study
proposes a novel, effective and efficient approach to deal with the information overload by
expanding existing trust networks so as to increase accuracy in recommendation systems.
More specifically, this study proposes a novel method to infer trust relationships, called
TriadicClosure. The method is based on the homophily phenomenon of social networks and,
more specifically, on the triadic closure mechanism, which is a fundamental mechanism of link
formation in social networks via which communities emerge naturally, especially when the
network is very sparse. Additionally, a method called JaccardCoefficient is proposed to
calculate the trust weight of the inferred relationships based on the Jaccard Cofficient
similarity measure. Both the proposed methods exploit structural information of the trust
graph to infer and calculate the trust value.
Experimental results on real-world datasets demonstrate that the TriadicClosure method
outperforms the existing state-of-the-art methods by substantially improving prediction
accuracy and coverage of recommendations. Moreover, the method improves the
performance of the examined state-of-the-art methods in terms of accuracy and coverage
when combined with them. On the other hand, the JaccardCoefficient method for calculating
the weight of the inferred trust relationships did not produce stable results, with the majority
showing negative impact on the performance, for both accuracy and coverage
Recent Advances in Social Data and Artificial Intelligence 2019
The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace